A conversation with the intellectual father of efficient market theory, Eugene Fama, passive portfolio management, and value and small cap mutual funds.

Few economists have had greater influence on financial theory, and practice, than Eugene Fama. His 1964 doctoral dissertation, “The Behavior of Stock Market Prices,” suggested that stock markets are efficient. Because of competition among investors, company share prices respond swiftly to past events, current information and future expectations. Actual prices are good estimates of intrinsic value, therefore, and no analyst can find consistent, profitable anomalies.

And price trends are random, said Fama. Past patterns don’t predict future directions. Published in a nontechnical article a year later, his research popularized the “efficient market hypothesis” and “random walk theory.”

Fama’s work soon transformed Wall Street, and later Main Street, by giving rise to a proliferation of low-cost index funds, as many questioned the value of paying for active portfolio management. “If one takes into account the higher initial loading charges of the [mutual] funds,” he observed over 40 years ago, “the random investment policy outperforms the funds.”

More recently, and often in collaboration with Dartmouth’s Kenneth French, Fama reexamined the capital asset pricing model, a classic model for determining fair cost for equity capital, and declared it an empirical failure unless two other factors, market capitalization and book value to market value, are also included. This work, too, has transformed Wall Street by providing academic support for “small-cap” and “value” funds.

Fama has done significant research in other areas, consistently sustaining connections between financial economics and macroeconomic theory. But his illumination of financial markets will undoubtedly be recognized as the University of Chicago scholar’s paramount contribution to both research and reality.

Principal-Agent Issues

Region: In the early 1980s, you authored three key pieces regarding
principal-agent conflicts [due to differing incentives of an
organization’s owners and employees] and how they play out efficiently
in various types of organizations. How have your ideas evolved in light
of transformations in the corporate world?

Fama: I haven’t spent a lot of time on these issues since then, but they
keep popping up. I haven’t seen anything that would cause me to change
my opinions generally, but something that has bothered me is the drying
up of the takeover market due to the installation of antitakeover
provisions by most companies, enabled by state legislatures.

Region: Poison pills and the like?

Fama: Right, and that is very unhealthy, I think, for the corporate
world because it takes away the threat of outside takeovers, which is
very important for the economy.

Region: A form of market discipline.

Fama: Yes, it’s a unique discipline that corporations have that other
forms of organization don’t have. For example, it’s very difficult to
attack the University of Chicago in that way. It doesn’t need a takeover
defense because there’s no real way to attack it. For a corporation, on
the other hand, there was a way. That allowed corporations to have
expert boards because the board wasn’t the court of last resort. But the
institution of all antitakeover amendments threw a wrench in the process.

CEO COMPENSATION

Region: Another issue those papers touched on was compensation of CEOs,
a controversial question in recent years. How do you view the suggestion
that
some CEOs are overcompensated?

Fama: If the [compensation] process gets captured by the CEO, then it
can get corrupted. But if what you’re seeing is a market wage, then I
don’t know why you would say it’s too high. If it’s a market wage, it’s
a market wage. I don’t know of any solid evidence that the process was
corrupted. So my premise would be that you’re just looking at market
wages. They may be big numbers; that’s not saying they’re too high. It’s
easy to say that people are paid too much, but when you’re on the other
side of the fence trying to hire high-level corporate managers, it turns
out not to be so easy.

EFFICIENT MARKET THEORY

Region: Can you give us a lay definition of the efficient market
hypothesis? How does it differ from random walk [the idea that movements
in stock prices are unpredictable]? And what is the genesis of efficient
market theory?

Fama: The basic wording of it is very simple. It says prices reflect all
available information. The conundrum is how to determine whether prices
reflect all available information, and you can’t do that without a model
of market equilibrium. What I added to the story was just pointing out
that you need a model of market equilibrium in order to carry out the
tests of market efficiency.

In the early 1960s, the advent of computers allowed people to do things
with data that they couldn’t do before. And the most easily available
data was stock market data. So lots of people started working on stock
market returns, and the question arose, well, what would we expect if
markets were working properly? “Random walk” was the first manifestation
of that. But it’s kind of a clumsy statement because it doesn’t
recognize that you need a model of market equilibrium to decide what the
market’s trying to do in setting prices.

Region: So that is the “joint hypothesis.”

Fama: Right. The joint hypothesis problem says that you can’t test
market efficiency without a model of market equilibrium. But the reverse
is also true. You can’t test models of market equilibrium without market
efficiency because most models of market equilibrium start with the
presumption that markets are efficient. They start with a strong version
of that hypothesis, that everybody has all relevant information. Tests
of market efficiency are tests of some model of market equilibrium and
vice versa. The two are joined at the hip.

Once I pointed that out, it was clear that the random walk model was
kind of irrelevant. You could have prices not following random walks
because the model of market equilibrium could generate expected returns
that had some predictable time-varying patterns to them. So the whole
nature of the game changed.

Region: The idea that things are unpredictable doesn’t necessarily mean
that they’re efficient.

Fama: No, not necessarily. Sure, prices could be random and still be
inefficient. Basically, what market efficiency says is that the
deviation of the realized price from the equilibrium expected value is
unpredictable based on any past information.

Region: Can you tell us about the genesis of this idea? You were at
Tufts University at the time, I believe.

Fama: When I was at Tufts, I was working for a professor who had a stock
market forecasting service. My job was to devise rules for predicting
the market, and I was very good at it. But he was a pretty good
statistician. He always told me to set some data aside so I could test
[the rules] out of sample. And they never worked out of sample.*

So when I came to the University of Chicago and people were talking
about these things, it suddenly dawned on me that maybe that was the
nature of the game, that there just wasn’t much predictability of
returns because markets were working efficiently. That was the beginning
of the story.

There were lots of people at Chicago and at MIT who were very interested
in that issue. Merton Miller. Franco Modigliani. Paul Cootner. Paul
Samuelson was very interested in it. And Benoît Mandelbrot.

Region: The subject of your first paper, I think.

Fama: Right. Half of my thesis was on the predictability of returns and
the other half was on the nature of the return distribution, which was
what Mandelbrot was all about, and still is.

IRRATIONAL EXUBERANCE

Region: Some economists—you know them well—say that the stock market
crash of 1929 and the more recent climb and decline of the market in the
early 2000s suggest that “irrational exuberance” affects the stock
market. How do you reconcile this alleged evidence of herding behavior
and animal spirits with the notion of market efficiency?

Fama: Well, economists are arrogant people. And because they can’t
explain something, it becomes irrational. The way I look at it, there
were two crashes in the last century. One turned out to be too small.
The ’29 crash was too small; the market went down subsequently. The ’87
crash turned out to be too big; the market went up afterwards. So you
have two cases: One was an underreaction; the other was an overreaction.
That’s exactly what you’d expect if the market’s efficient.

The word “bubble” drives me nuts. For example, people say “the Internet
bubble.” Well, if you go back to that time, most people were saying the
Internet was going to revolutionize business, so companies that had a
leg up on the Internet were going to become very successful.

I did a calculation. Microsoft was an example of a corporation that came
from the previous revolution, the computer revolution. It was hugely
profitable and successful. How many Microsofts would it have taken to
justify the whole set of Internet valuations? I think I estimated it to
be something like 1.4.

Region: About one and a half Bill Gateses.

Fama: That’s right. And Microsoft was a good example because the worse
their products were, the more money they made [laughter]. Who didn’t
struggle with DOS and then the first versions of Windows?

INTERNATIONAL EQUITY MARKETS

Region: Are all stock markets equally efficient? Is the Hang Seng as
efficient as the Nasdaq as the Australian stock exchange? If not, are
there money-making opportunities internationally that don’t exist in the
United States?

Fama: Anybody who has studied that issue doesn’t come to the conclusion
that there are huge opportunities in other markets that don’t exist in
the United States. That’s kind of a standard line of international money
managers, that the opportunities are better in international markets.
That’s certainly not true in developed markets.

In emerging markets, well, I think maybe insiders have more information
than they do in domestic markets, but maybe not. In any case, there’s
not enough data to know about emerging markets. And the variances are so
big it would be impossible to know anyway. When people study money
managers in developed markets, they don’t find any evidence that those
markets are inefficient … and there’s very little evidence that they’re
inefficient in the United States. But I’ve never taken the extreme
position that markets are entirely efficient.

HAS FAMA SOFTENED?

Region: I was going to ask you about that. As you know, I’m sure, there
was a lengthy Wall Street Journal profile of you and your colleague
Richard Thaler in 2004, suggesting that you had softened a bit.

Fama: [Laughter].

Region: And I’m wondering if that was accurate or if you’ve always
believed that markets are less than perfectly efficient?

Fama: I start my class every year by saying, “These are models. And the
reason we call them models is that they’re not 100 percent true. If they
were, we would call them reality, not models. They’re simplifications.”
But the acid test is, How good are the simplifications for your
purposes? And for almost all purposes, market efficiency is a very good
approximation. There is very little evidence that money managers can
beat the market.

DIMENSIONAL FUND ADVISORS

Region: Then this is the right time to ask, I guess, about Dimensional
Fund Advisors, on whose board you sit. Why should an investor pay a
management fee to Dimensional Fund when an index fund might provide
efficient returns at lower cost?

Fama: Well, Dimensional is a passive manager. They don’t charge high
fees. Vanguard, for example, is another passive manager that charges
very low fees. You shouldn’t pay managers very much. The average
management fee for an actively managed mutual fund is about 1 percent.
There’s no evidence that they generate anything for that 1 percent. So
my answer is, I don’t know why anybody buys them.

Region: And yet we keep doing it, don’t we?

Fama: Well, people want to think there’s money left on the table for them.

THREE-FACTOR MODEL

Region: With Kenneth French, you’ve said that the capital asset pricing
model (CAPM) developed by John Lintner and William Sharpe has “fatal
problems” in explaining stock market returns because of its reliance on
beta [the volatility of an individual stock relative to overall market
volatility]. And you’ve found that two other factors are crucial for
determining prices. Can you tell us about these factors? Are they
inefficiencies, or do they represent hidden risk? And is the CAPM truly
dead?

Fama: Let me first tell you what the returns evidence says, and then we
can talk about how to interpret it. The returns evidence basically says
that if you look at the CAPM market beta, it’s not enough to describe
the cross section of average returns.

The CAPM says that all you need to know are these market betas, market
sensitivities, in order to fully describe the cross section of average
returns. What you find is that other variables contribute to the
explanation of average returns above and beyond what you get from beta.
Indeed, over the last 50 years, you get very little at all from beta.

The two variables that we’ve focused on are market capitalization (the
financial profession calls it size, a misnomer because it’s really
market capitalization) and the book-to-market ratio, the ratio of the
book value of a common equity to its market value. Now, there’s no magic
in that ratio. The ratio of almost anything to price will work as well.
These are the two variables.

So, small-cap stocks have higher average returns than large-cap stocks,
and stocks with higher ratios of book value to market value have higher
returns than low book-to-market stocks. Low book-to-market stocks tend
to be growth stocks. High book-to-market stocks tend to be relatively
more distressed; they’re what people call value stocks. That’s given
rise to what the finance profession—academic as well as applied—calls
the size premium and the value premium. The value premium tends to be
bigger.

So the issue then is, Are these risk factors or market inefficiencies?
One group of people says they’re market inefficiencies—particularly the
value premium. The behaviorists tend to say the value premium is a
market inefficiency. Their story is: The market overreacts to good and
bad past times. It doesn’t understand that things tend to mean revert.
So growth companies that have done very well tend to be overpriced, and
value companies that have done poorly tend to be underpriced, and then
the market realizes this and corrects it. And this story says,
basically, that people are dumb; they never learn. So every generation
of growth stocks and value stocks goes through the same sort of cycle.

That’s not too appealing to an economist—the idea that people never
learn about these things—but that is the behavioral story. And initially
they said these are arbitrage opportunities because if you go long value
stocks and short growth stocks, you get something with a variance close
to zero.

But French and I pointed out that if you do that, you get something with
a variance very close to the market variance, not zero. It’s quite a
risky strategy. And the premium is about the size of the market premium.
So it looks and smells like a risk premium. And we developed a
three-factor model with a size premium in addition, basically the
difference between the returns on small stocks and big stocks.

So, our model has three factors. Every asset pricing model says you need
the market in there. Then they differ on how many other things you need.
The CAPM says you only need the market. We basically say a minimum of
two other factors seem to be necessary. And these two do a pretty good job.

There’s still a third explanation, which is not based on overreaction.
It says that people just don’t like small stocks and value stocks.
There’s some amount of utility that people get from the nature of the
stocks that they hold. So they like big stocks and they like growth
stocks, and they’re willing to hold them even though they have lower
average returns.

Now you can’t have an arbitrage opportunity there because then there’d
be a sure profit. But the fact that they look like risk factors can
sustain that story. You can’t tell the difference between that story and
a risk story.

MOMENTUM

Region: Let me ask you about momentum. You’ve said that it’s the
strongest challenge to the hypothesis of market efficiency. Can you
elaborate on that?

Fama: There’s evidence that if you rank stocks every month based on
their last year of returns, the very extreme winners tend to win for a
few more months and the losers tend to lose for a few more months.

That seems to be true in U.S. data beginning around 1950. We don’t have
foreign data going back that far, but it tends to be there in major
foreign markets except for Japan. It doesn’t tend to be there in the
U.S. data for the ’30s and ’40s. So there’s some chance that it is just
a chance result. There are so many people looking for anomalies in the
data, that may just be the biggest one that they’ve found. Maybe it
won’t be there in the future. We don’t know yet.

Region: Is there an opportunity to make money there?

Fama: Well, there isn’t much of an opportunity to make money, because as
I said, you do this every month. And if you rank and trade stocks every
month, the turnover of these portfolios is enormous.

Region: The costs will eat up the profits.

Fama: Right. The costs will kill you. So the people who have written
these papers have said, basically, “This is interesting, but forget
about trading on it.” But it’s still interesting.

HOUSING MARKETS

Region: Your efficient market hypothesis applies to stocks, of course.
Recent events have led to scrutiny of housing markets. Are housing
markets efficient? Is there greater potential for irrationality to crop
up there, either because housing investors are less sophisticated than
stock market investors or because housing markets are less liquid?

Fama: I don’t know. Housing markets are less liquid, but people are very
careful when they buy houses. It’s typically the biggest investment
they’re going to make, so they look around very carefully and they
compare prices. The bidding process is very detailed. The bottom line is
that real estate is a huge component of wealth and we have no data on
it. So the answer to your question is, Who knows?

CREDIT MARKETS

Region: Some observers have suggested that regulators and others have
put too much reliance on ratings agencies to determine the risk of
mortgage-backed securities and that even financially sophisticated
parties “didn’t really know what they were buying.” Is this evidence
that credit markets are inefficient?

Fama: That story just doesn’t appeal to me. First of all, it’s well
known that rating agencies tend to lag actual changes in credit
worthiness. For example, stock prices predict changes in ratings better.
The best models of credit quality are basically options pricing models
that work off the stock price. So I’m very skeptical of these stories.

The bond market is a simpler market than the stock market. Bonds are
simpler to evaluate than stocks, because there’s downside risk, but you
don’t have to worry much about the upside: They’re not going to pay you
more than they promised. So bonds are much simpler to deal with. Now
bond products have become more complicated because of the securitization
of that market, but still not that big a deal.

NEW FINANCIAL TECHNOLOGIES

Region: What about hedge funds, collateralized debt obligations and
other newer financial technologies—do they serve a useful purpose in
mitigating market risk, or do they heighten it?

Fama: I don’t know. People talk on both sides of that issue. The problem
is that we don’t have very good hedge fund data and the data we have
only goes back about 10 years. That’s just not enough to come to any
conclusions on these issues. So I don’t know if it’s going to take
another half century before we really know. You’re talking about returns
with such high volatility that it really is going to take that long.

This is a standard part of a talk that I give to investment
professionals: People like to tell stories about short periods of data,
but the reality is that you can’t measure the market premium over
periods shorter than an investment lifetime. The 5 percent stock market
premium over bills takes about 35 years before it becomes two standard
errors from zero.

EQUITY PREMIUM

Region: How do you explain the equity premium puzzle [the idea that
stocks should in theory provide only a 1 percent higher annual return
than bonds, but have historically returned nearly 7 percent more]?

Fama: In terms of these consumption-based asset pricing model stories?
What I say to the consumption people is: You’re telling me the premium
should be about 1 percent a year. Well, you wouldn’t be able to tell the
difference between that and zero over a 1,000-year period. And for a 1
percent a year premium, who do you know that would hold stocks? It’s
this representative investor, but who is that guy anyway? I wouldn’t
hold them. I don’t know anybody else who would. So there’s got to be
something missing in those models.

ARE BANKS STILL SPECIAL?

Region: In 1985, you wrote a paper titled “What’s Different About
Banks?” It’s a question often discussed by the Fed, for obvious reasons.
You wrote that special monitoring services and special transactions
services, including the checking system, are part of what makes them
unique. As other nonbank organizations take over some of the roles, are
banks no longer so different, no longer as special?

Fama: Excellent question. Basically, the only companies that can issue
debt publicly are very large companies. I mean directly issue debt,
commercial paper or marketable bonds. Everybody else has to go to an
institution. Now what institutions have done is to securitize these
things, put them into bundles and put them on the market. Lots of people
have been working on the extent to which the monitoring function as a consequence has been diluted somewhat because the banks aren’t holding
100 percent of the paper that they create. So that’s an ongoing issue. I
don’t know what the answer is about whether banks are less relevant now.
They’re doing a lot more different things than they ever did, but so are
all financial institutions.

SSRN

Region: You and Michael Jenson helped create the Financial Economics
Network, which then broadened into the Social Science Research Network.
What role do you see it playing in the future in the creation and
distribution of economic research?

Fama: I think it’s great for working papers, but I don’t think you can
do without the refereeing process. Will all journals end up online? I
think that’s a good possibility. But the refereeing process is still
critical for quality, improving work and certifying work. So
professional journals may change in nature, but that function will
remain, and the editorial function will remain as a consequence.

FINANCE/MACRO

Region: It seems to me that macroeconomists are paying more attention
these days to the work of financial economists, especially in trying to
understand asset pricing. Do financial economists find equal value in
the macro theory work being done these days?

Fama: I think those two areas have always been pretty closely joined. I,
in fact, wrote a lot of macro-related stuff in the ’80s or even earlier.
For example, rational expectations stuff is basically efficient markets;
they’re pretty much the same thing. If you’re talking about the
macroeconomy, I don’t see how you can avoid financial markets. That’s a
big part of the game. Nobody talks about money and bonds anymore the way
they did when I was taking macroeconomics. Now people realize it is a
lot more complicated. Finance and macro are joined. Our finance faculty
has several people who were trained as macroeconomists, especially on
the asset pricing side.

TIME ALLOCATION

Region: I understand that you work every day, even holidays. Is that right?

Fama: Right.

Region: That’s an amazing work ethic.

Fama: Not really.

Region: I’ve also heard that you’re a dedicated athlete.

Fama: Right. I work every day, but I never work a full day. I get up at five o’clock in the morning and I work basically all morning until maybe
one o’clock, two o’clock, and then I go play golf, I go windsurfing, I
play tennis. And that’s it.

Region: We should let you go then. Thank you very much.

—Douglas Clement
Nov. 2, 2007

* When building forecasting models, statisticians often partition their data. The in-sample portion is used to develop the model, and the out-of-sample batch is then used to test the model's predictive ability.